Motivation: More Precise Timestamps

R has excellent facilities for dealing with both dates and datetime objects. For datetime objects, the POSIXt time type can be mapped to POSIXct and its representation of fractional seconds since the January 1, 1970 “epoch” as well as to the broken-out list representation in POSIXlt. Many add-on packages use these facilities.

POSIXct uses a double to provide 53 bits of resolution. That is generally good enough for timestamps down to just above a microsecond, and has served the R community rather well.

But increasingly, time increments are measure in nanoseconds. Other languages uses a (signed) 64-bit integer to represent (integer) nanoseconds since the epoch. A bit over a year I realized that we have this in R too—by combining the integer64 type in the bit64 package by Jens Oehlschlaegel with the CCTZ-based parser and formatter in my RcppCCTZ package. And thus the nanotime package was created.

Leonardo Silvestri then significantly enhanced nanotime by redoing it as an S4 class.

A simple example:

library(nanotime)
n <- nanotime(42)
n

[1] "1970-01-01T00:00:00.000000042+00:00"

Here we used a single element with value 42, and created a nanotime vector from it—which is taken to me 42 nanoseconds since the epoch, or basically almost at January 1, 1970.

Step 1: Large Integer Types

So more recently I had a need to efficiently generate such integer vector from int64_t data. Both Leonardo and Dan helped with initial discussion and tests. One can either use a reinterpret_cast<> or a straight memcpy as the key trick in bit64 is to use the underlying 64-bit double. So we have the space, we just need to ensure we copy the bits rather than their values. This leads to the following function to create an integer64 vector for use in R at the C++ level:

#include <Rcpp.h>
                                        
Rcpp::NumericVector makeInt64(std::vector<int64_t> v) {
    size_t len = v.size();
    Rcpp::NumericVector n(len);         // storage vehicle we return them in

    // transfers values 'keeping bits' but changing type
    // using reinterpret_cast would get us a warning
    std::memcpy(&(n[0]), &(v[0]), len * sizeof(double));

    n.attr("class") = "integer64";
    return n;
}

This uses the standard trick of setting a class attribute to set an S3 class. Now the values in v will return to R (exactly how is treated below), and R will treat the vector as integer64 object (provided the bit64 package has been loaded).

Step 2: Nanotime

A nanotime vector is creating using an internal integer64 vector. So the previous functions almost gets us there. But we need to set the S4 type correctly. So that needed some extra work. The following function does it:

#include <Rcpp.h>

Rcpp::S4 makeNanotime(std::vector<int64_t> v) {
    size_t len = v.size();
    Rcpp::NumericVector n(len);         // storage vehicle we return them in

    // transfers values 'keeping bits' but changing type
    // using reinterpret_cast would get us a warning
    std::memcpy(&(n[0]), &(v[0]), len * sizeof(double));

    // do what needs to be done for the S4-ness: class, and .S3Class
    // this was based on careful reading of .Internal(inspect(nanotime(c(0,1))))
    Rcpp::CharacterVector cl = Rcpp::CharacterVector::create("nanotime");
    cl.attr("package") = "nanotime";
    n.attr(".S3Class") = "integer64";
    n.attr("class") = cl;
    SET_S4_OBJECT(n);

    return Rcpp::S4(n);
}

This creates a nanotime vector as a proper S4 object.

Step 3: Returning them R via data.table

The astute reader will have noticed that neither function had an Rcpp::export tag. This is because of the function argument: int64_t is not representable natively by R, which is why we need a workaround. Matt Dowle has been very helpful in providing excellent support for nanotime in data.table (even after we, ahem, borked it by switching from S3 to S4). This support was of course relatively straightforward because data.table already had support for the underlying integer64, and we had the additional formatters etc.

#include <Rcpp.h>

// Enable C++11 via this plugin (Rcpp 0.10.3 or later)
// [[Rcpp::plugins("cpp11")]]

Rcpp::NumericVector makeInt64(std::vector<int64_t> v) {
    size_t len = v.size();
    Rcpp::NumericVector n(len);         // storage vehicle we return them in

    // transfers values 'keeping bits' but changing type
    // using reinterpret_cast would get us a warning
    std::memcpy(&(n[0]), &(v[0]), len * sizeof(double));

    n.attr("class") = "integer64";
    return n;
}

Rcpp::S4 makeNanotime(std::vector<int64_t> v) {
    size_t len = v.size();
    Rcpp::NumericVector n(len);         // storage vehicle we return them in

    // transfers values 'keeping bits' but changing type
    // using reinterpret_cast would get us a warning
    std::memcpy(&(n[0]), &(v[0]), len * sizeof(double));

    // do what needs to be done for the S4-ness: class, and .S3Class
    // this was based on careful reading of .Internal(inspect(nanotime(c(0,1))))
    Rcpp::CharacterVector cl = Rcpp::CharacterVector::create("nanotime");
    cl.attr("package") = "nanotime";
    n.attr(".S3Class") = "integer64";
    n.attr("class") = cl;
    SET_S4_OBJECT(n);

    return Rcpp::S4(n);
}

// [[Rcpp::export]]
Rcpp::DataFrame getDT() {
    std::vector<int64_t> d = { 1L, 1000L, 1000000L, 1000000000L };
    std::vector<int64_t> ns = { 1510442294123456789L, 1510442295123456789L, 
        1510442296123456789L, 1510442297123456789L };
        
    Rcpp::DataFrame df = Rcpp::DataFrame::create(Rcpp::Named("int64s") = makeInt64(d),
                                                 Rcpp::Named("nanos") = makeNanotime(ns));
    df.attr("class") = Rcpp::CharacterVector::create("data.table", "data.frame");
    return(df);
}

Example

The following example shows the output from the preceding function:

suppressMessages(library("data.table"))
dt <- getDT()
print(dt)

       int64s                               nanos
1:          1 2017-11-11T23:18:14.123456789+00:00
2:       1000 2017-11-11T23:18:15.123456789+00:00
3:    1000000 2017-11-11T23:18:16.123456789+00:00
4: 1000000000 2017-11-11T23:18:17.123456789+00:00

dt[[1]]

integer64
[1] 1          1000       1000000    1000000000

dt[[2]]

[1] "2017-11-11T23:18:14.123456789+00:00"
[2] "2017-11-11T23:18:15.123456789+00:00"
[3] "2017-11-11T23:18:16.123456789+00:00"
[4] "2017-11-11T23:18:17.123456789+00:00"

diff(dt[[2]]) # here 1e9 nanoseconds between them

integer64
[1] 1000000000 1000000000 1000000000